LangChain
Deployed Engineer (Charlotte)
About this role
LangChain is seeking a Deployed Engineer in Charlotte to work directly with enterprise customers building production AI agents. You'll co-architect solutions, own technical evaluations, and help customers deploy and operate agent-based systems reliably at scale using the LangChain platform.
What you'll do
- Co-architect and build production AI agents with customer engineering teams
- Own technical wins in pre-sales by designing POCs and guiding evaluations
- Deploy and operate agent-based applications including conversational and research agents
- Provide post-sale advisory on architecture, best practices, and technical decisions
- Deliver technical demos, trainings, and workshops for developer audiences
- Surface field feedback and contribute reusable patterns and example code
What they're looking for
- Python and JavaScript programming
- Agent-based and LLM application design
- Multi-step workflow orchestration and failure handling
- Direct customer engagement and technical communication
- Explaining technical tradeoffs clearly
- Cloud environments (AWS, GCP, Azure)
- Production software deployment and operations
- LangChain, LangGraph, or similar frameworks
Benefits
- Work on production AI systems with real-world impact
- Fast feedback loops and visible results
- Opportunity to shape AI agent adoption in the field
- Intersection of engineering, product, and go-to-market
- Access to Fortune 500 customer problems and use cases
- Startup environment with Series B funding and growth trajectory
Opens the application — the Jobs AI extension fills it for you. Set up autofill
Opens the official application on the employer’s site. No login required.
LangChain
LangChain builds platforms and frameworks for developing, deploying, and observing production AI agents at enterprise scale, including LangSmith for AI observability and evaluation. The company is hiring Deployed Engineers to work directly with enterprise customers on agent implementation and operations, as well as Fullstack Engineers to build features across its platform stack.
- Website
- langchain.com
Likely interview questions
- Walk us through a production AI agent or LLM application you've built. What were the hardest technical problems you solved, and how did you handle failure cases?
- Describe your experience designing agent architectures. How do you approach orchestration, multi-step workflows, and deciding between different patterns?